code: def loadModel(
      url="https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5",
  ) -> Model:
      base_model = ResNet34()
      inputs = base_model.inputs[0]
      arcface_model = base_model.outputs[0]
      arcface_model = BatchNormalization(momentum=0.9, epsilon=2e-5)(
          arcface_model
      )
      arcface_model = Dropout(0.4)(arcface_model)
      arcface_model = Flatten()(arcface_model)
      arcface_model = Dense(
          512,
          activation=None,
          use_bias=True,
          kernel_initializer="glorot_normal",
      )(arcface_model)
      embedding = BatchNormalization(
          momentum=0.9, epsilon=2e-5, name="embedding", scale=True
      )(arcface_model)
      model = Model(inputs, embedding, name=base_model.name)
  
      # ---------------------------------------
      # check the availability of pre-trained weights
  
      home = functions.get_deepface_home()
  
      file_name = "arcface_weights.h5"
      output = home + "/.deepface/weights/" + file_name
  
      if os.path.isfile(output) != True:
          logger.info(f"{file_name} will be downloaded to {output}")
          gdown.download(url, output, quiet=False)
  
      # ---------------------------------------
  
      model.load_weights(output)
  
      return model
  
file_path: deepface/deepface/basemodels/ArcFace.py
imports: import os
  import gdown
  import tensorflow
  from deepface.commons import functions
  from deepface.commons.logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: loadModel(url) and returns: model